Search

Virtual current sensors (VCSs) are promising solutions for accurate current measurement in high performance and low cost permanent magnet synchronous machine (PMSM) drives. They not only help identify faults in hardware current sensors, but also ensure the continuous operation of PMSM drive system with reduced hardware current sensor counts. However, traditional observer based VCSs have limitations in terms of slow response and large overshoot, which could lead to detection errors in condition monitoring of hardware current sensors and degraded control performance when VSCs are employed in the control loop. This article proposes an enhanced nonlinear observer based VCSs approach for PMSM drives. A novel nonlinear function with a smooth inflection point is introduced to design the nonlinear observer. This nonlinear function enables an adaptive observer gain update corresponding to the current error, which contributes to a varied damping ratio in the current observer for improved dynamics. As a result, the proposed enhanced nonlinear observer (ENO) based VCSs approach achieves fast response with small current overshoot. The proposed ENO based VCSs are validated with both simulation and experimental test results on a laboratory interior PMSM drive in different VCS application scenarios.

Within smart grids, end-users often seek to reduce electricity costs using a home energy management system, while the power utility aims to optimize the grid operation. This article proposes a novel three-level framework to tackle the contradictory nature of the objectives of end-user and distribution system operators (DSOs) by upward and downward flexibility provision. In addition, the proposed method designs a novel incentive program based on the flexibility provided by the end-users. Then, the aggregated flexibility is offered to the DSO. The model takes advantage of the local flexibility of home appliances to re-schedule their use based on the flexibility request from the DSO. The aggregator provides incentive programs based on the flexibility provided by the prosumers to reduce/shift energy consumption. The proposed methodology considers the alignment of the conflicting techno-economic objectives of the prosumers and system operators. The results show that our proposed strategy has increased the monetary benefits for prosumers for their flexibility services provided to the DSO compared to other scenarios. Moreover, the proposed method improves the voltage profiles and reduces the peak load and power losses by 20.8% and 24.6%, respectively, of the overall system.

For permanent magnet synchronous machine (PMSM) drive, accurate magnet temperature is critical. The popular model-based magnet temperature estimation can be affected by core loss effect especially in the high-speed conditions. This article proposes a novel hybrid approach for accurate magnet temperature modeling and estimation, in which the estimation model is established by tracking the flux linkage variation, while the data-driven-based model is proposed to compensate the core loss effect. Specifically, the flux linkages in the rotating frame are projected into a new frame to derive the estimation model establishing the relationship between flux linkage variation and magnet temperature, in which the inverter distortion effect is canceled to improve the model accuracy. Based on this estimation model, the core loss effect is modeled, which indicates that the core loss influence is highly nonlinear and dependent on operating conditions. Hence, a radial basis function-based network is employed to model and compensate the core loss effect, and the network training is derived from the proposed model. The proposed hybrid approach can effectively improve the estimation performance especially at the high-speed conditions. Extensive experiments and comparisons are conducted on a laboratory interior PMSM drive to evaluate the proposed approach under various operating conditions.